A clustering approach to detect multiple outliers in linear functional relationship model for circular data
Outlier detection has been used extensively in data analysis to detect anomalous observation in data. It has important applications such as in fraud detection and robust analysis, among others. In this paper, we propose a method in detecting multiple outliers in linear functional relationship model...
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| Format: | Article |
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Taylor & Francis
2018
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| _version_ | 1825721647320006656 |
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| author | Mokhtar, Nurkhairany Amyra Zubairi, Yong Zulina Hussin, Abdul Ghapor |
| author_facet | Mokhtar, Nurkhairany Amyra Zubairi, Yong Zulina Hussin, Abdul Ghapor |
| author_sort | Mokhtar, Nurkhairany Amyra |
| collection | UM |
| description | Outlier detection has been used extensively in data analysis to detect anomalous observation in data. It has important applications such as in fraud detection and robust analysis, among others. In this paper, we propose a method in detecting multiple outliers in linear functional relationship model for circular variables. Using the residual values of the Caires and Wyatt model, we applied the hierarchical clustering approach. With the use of a tree diagram, we illustrate the detection of outliers graphically. A Monte Carlo simulation study is done to verify the accuracy of the proposed method. Low probability of masking and swamping effects indicate the validity of the proposed approach. Also, the illustrations to two sets of real data are given to show its practical applicability. |
| first_indexed | 2024-03-06T05:53:32Z |
| format | Article |
| id | um.eprints-21223 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:53:32Z |
| publishDate | 2018 |
| publisher | Taylor & Francis |
| record_format | dspace |
| spelling | um.eprints-212232019-05-14T06:18:54Z http://eprints.um.edu.my/21223/ A clustering approach to detect multiple outliers in linear functional relationship model for circular data Mokhtar, Nurkhairany Amyra Zubairi, Yong Zulina Hussin, Abdul Ghapor Q Science (General) QA Mathematics Outlier detection has been used extensively in data analysis to detect anomalous observation in data. It has important applications such as in fraud detection and robust analysis, among others. In this paper, we propose a method in detecting multiple outliers in linear functional relationship model for circular variables. Using the residual values of the Caires and Wyatt model, we applied the hierarchical clustering approach. With the use of a tree diagram, we illustrate the detection of outliers graphically. A Monte Carlo simulation study is done to verify the accuracy of the proposed method. Low probability of masking and swamping effects indicate the validity of the proposed approach. Also, the illustrations to two sets of real data are given to show its practical applicability. Taylor & Francis 2018 Article PeerReviewed Mokhtar, Nurkhairany Amyra and Zubairi, Yong Zulina and Hussin, Abdul Ghapor (2018) A clustering approach to detect multiple outliers in linear functional relationship model for circular data. Journal of Applied Statistics, 45 (6). pp. 1041-1051. ISSN 0266-4763, DOI https://doi.org/10.1080/02664763.2017.1342779 <https://doi.org/10.1080/02664763.2017.1342779>. https://doi.org/10.1080/02664763.2017.1342779 doi:10.1080/02664763.2017.1342779 |
| spellingShingle | Q Science (General) QA Mathematics Mokhtar, Nurkhairany Amyra Zubairi, Yong Zulina Hussin, Abdul Ghapor A clustering approach to detect multiple outliers in linear functional relationship model for circular data |
| title | A clustering approach to detect multiple outliers in linear functional relationship model for circular data |
| title_full | A clustering approach to detect multiple outliers in linear functional relationship model for circular data |
| title_fullStr | A clustering approach to detect multiple outliers in linear functional relationship model for circular data |
| title_full_unstemmed | A clustering approach to detect multiple outliers in linear functional relationship model for circular data |
| title_short | A clustering approach to detect multiple outliers in linear functional relationship model for circular data |
| title_sort | clustering approach to detect multiple outliers in linear functional relationship model for circular data |
| topic | Q Science (General) QA Mathematics |
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